PATIENT SPECIFIC MODELS IN LUNG CANCER SCREENING WITH CT

CT 肺癌筛查中的患者特异性模型

• 批准号: 6628487
• 负责人: MATTHEW S BROWN
• 金额: $ 27.41万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-16 至 2005-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6628487
• 关键词: biomedical automation; clinical research; computed axial tomography; computer assisted diagnosis; computer system design /evaluation; diagnosis design /evaluation; human subject; image processing; lung neoplasms; neoplasm /cancer diagnosis

DESCRIPTION (Verbatim from Applicant's Abstract): The objective of this research is to develop computer-assisted methods to facilitate screening for the early detection of lung cancer using helical computed tomography (hCT). Proponents of existing screening trials argue that the highest enhance of surgical cure from lung cancer lies in the detection of micronodular neoplasms (of 1-3 mm in diameter). Multi-slice hCT is capable of imaging the entire thorax at high spatial resolution and has the potential to reliably detect pulmonary micronodules. However, these image sequences generate extremely large volume data sets, consisting of 300-600 axial images, that are impractical to review in current radiology practice. This proposal involves development and experimental testing of a method to automatically identify lung nodules from high resolution hCT (HR-hCT) image data acquired from multi-slice scanners. The technique involves a model-based segmentation approach in which information about the size, shape, location, density and other properties of both normal and pathological structures will be used to automate the discrimination of focal lung nodules from normal bronchovascular anatomy. A generic, a priori model of lung nodules and relevant anatomy will be developed to guide segmentation of baseline CT images. Patient-specific models will be derived from the anatomical information learned from baseline scans and used to analyze subsequent surveillance CT scans. The specific aims to accomplish this are: [1] To automatically distinguish lung nodules from normal pulmonary bronchovascular structures on baseline lung cancer screening HR-hCT exams. [2] To detect interval new nodules and re-localize previously detected nodules on post-baseline surveillance HR-hCT exams. [3] To measure the accuracy of automated nodule detection and re-localization on HR-hCT scans. [4] To compare radiologist accuracy and interpretation times of HR-hCT scans, both with and without assistance from the automated detection system, against pre-existing nodule detection methods.

描述（逐字摘自申请人摘要）：此目的 研究的目的是开发计算机辅助方法来促进筛查 使用螺旋计算机断层扫描 (hCT) 早期检测肺癌。 现有筛查试验的支持者认为， 肺癌的手术治愈在于微结节性肿瘤的检测 （直径1-3毫米）。多层 hCT 能够对整个组织进行成像 胸部具有高空间分辨率，并且有可能可靠地检测 肺部微结节。然而，这些图像序列会产生非常大的 体积数据集，由 300-600 个轴向图像组成，这对于 回顾当前放射学实践。 该提案涉及一种方法的开发和实验测试 从高分辨率 hCT (HR-hCT) 图像中自动识别肺结节 从多层扫描仪获取的数据。该技术涉及基于模型的 分割方法，其中有关大小、形状、位置的信息， 正常和病理结构的密度和其他特性将是 用于自动区分局灶性肺结节与正常肺结节 支气管血管解剖学。肺结节和相关的通用先验模型 将开发解剖学来指导基线 CT 图像的分割。 患者特定模型将从所学到的解剖信息中得出 来自基线扫描并用于分析后续的监视 CT 扫描。 实现这一目标的具体目标是： [1] 自动区分肺结节和正常肺结节 基线肺癌筛查 HR-hCT 检查中的支气管血管结构。 [2] 检测间隔新结节并重新定位先前检测到的结节 基线后监测 HR-hCT 检查。 [3] 测量自动结节检测和重新定位的准确性 HR-hCT 扫描。 [4] 为了比较放射科医生 HR-hCT 扫描的准确性和判读时间， 无论有或没有自动检测系统的帮助， 预先存在的结节检测方法。